Product Description

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.

The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

* Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface

Product Details

• Amazon Sales Rank: #46312 in Books
• Published on: 2005-07-13
• Original language: English
• Number of items: 1
• Binding: Paperback
• 560 pages

Review
"This book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples, and urging the reader to consider the simple techniques first, and then progress to the more sophisticated ones if the simple ones prove inadequate. If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start."
- From the foreword by Jim Gray, Microsoft Research

"It covers cutting-edge, data mining technology that forward-looking organizations use to successfully tackle problems that are complex, highly dimensional, chaotic, non-stationary (changing over time), or plagued by. The writing style is well-rounded and engaging without subjectivity, hyperbole, or ambiguity. I consider this book a classic already!"
- Dr. Tilmann Bruckhaus, StickyMinds.com

About the Author

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.

Eibe Frank is a researcher in the Machine Learning group at the University of Waikato. He holds a degree in computer science from the University of Karlsruhe in Germany and is the author of several papers, both presented at machine learning conferences and published in machine learning journals.

Customer Reviews

Revised and ready to lead you down a good path
Having read the first edition, the authors earn the extra rating because they've managed to improve on their work and practical WEKA resource offering. Without a doubt, an essential read for people who are both new and experienced in the fields of data mining, descriptive & predictive analytics or state & behavioural modelling.

The volume of material on the market today is still quite limited and in the gap between the first and second edition of this book, quite a lot has actually changed in the field. In my view, book content has only marginally progressed with the times, perhaps in favour of attempting to attract and activate new members, practictioners and commercially oriented researchers to the fore of data mining. It's a bold step to evolve material as the field evolves; those breaking new ground in this area should be more visible and offered greater support.

I believe that there is room in the market now for some revised materials covering anomalised commercial implementations of Advanced Data Mining & AI Concepts. A small community of authors could plug this gap really well.